Must-know Machine Learning Questions - Logistic Regression - upGrad blog 19) Suppose, You applied a Logistic Regression model on a given data and got a training accuracy X and testing accuracy Y. c) The cost function of logistic regression is concave d) The cost function of logistic regression is convex Answer: (d) The cost function of logistic regression is convex Gradient descent will converge into global minimum only if the cost function is convex in the case of logistic regression. A. Logistic Regression is very easy to understand. Here, there are five variables for which the coefficients are given. So, for Logistic Regression the cost function is If y = 1 Logistic regression cost function For logistic regression, the C o s t function is defined as: C o s t ( h ( x), y) = { log ( h ( x)) if y = 1 log ( 1 h ( x)) if y = 0 By optimising this cost function, convergence is achieved. There should be a linear relationship between the logit of the outcome and each. Well, it turns out that for logistic regression we just have to find a different C o s t function, while the summation part stays the same. Logistic regression predicts the output of a categorical dependent variable. 30 Questions to test your understanding of Logistic Regression ML | Cost function in Logistic Regression - GeeksforGeeks It can be either Yes or No, 0 or 1, true or False, etc. Answer: D. Explanation: All of the above are are the advantages of Logistic Regression. . Therefore the outcome must be a categorical or discrete value. Xp is linear. Questions On Logistic Regression - Analytics Vidhya So let's fit the. 7. Logistic Regression Cost function is "error" representa. Logistic Regression in Machine Learning - Javatpoint Cost Function in Logistic Regression | by Brijesh Singh - Medium b) Logistic regression. A Sigmoid B Mode C Square D All of the above 7 Which of the following method (s) does not have closed form solution for its coefficients? The Problem of Convexity The confident right predictions are rewarded less. Machine Learning MCQ - Cost function of logistic regression is convex C. It performs well for simple datasets as well as when the data set is linearly separable. d) Greedy algorithms. It requires less training. Solution: A. Model will become very simple so bias will be very high. Discuss the space complexity of Logistic Regression. Hence, the log odds become: ln (P1P) = 0.47 X1 0.45 X2+0.39 X30.23 X4+0.55 X5 As you can see, we have ignored the 0 since it will be the same for all the three consumers. In the cost function for logistic regression, the confident wrong predictions are penalised heavily. By optimising this cost function, convergence is achieved. Let's now imagine that we apply to this logistic model the same error function that's typical for linear regression models. Why Does the Cost Function of Logistic Regression Have a - Baeldung Finding the global minimum in such cases using gradient descent is not possible. Cross-entropy or log loss is used as a cost function for logistic regression. Finding the global minimum in such cases using gradient descent is not possible. It assumes that the dependence of Y on X1, X2, . The cost function in logistic regression - Internal Pointers But this results in cost function with local optima's which is a very big problem for Gradient Descent to compute the global optima. linear regression is an incredibly powerful tool for analysing data. During training: We need to store four things in memory: x, y, w, and b during training a Logistic Regression model. In the case of Linear Regression, the Cost function is - But for Logistic Regression, It will result in a non-convex cost function. We can write as: 4.2. This function normally consists of the mean squared error () between the model's predictions and the values of the target variable. Logistic Regression Interview Questions It is used for predicting the categorical dependent variable using a given set of independent variables. Due to this reason, MSE is not suitable for logistic regression. Storing b is just 1 step, i.e, O (1) operation since b is a constant. Learn what is Logistic Regression Cost Function in Machine Learning and the interpretation behind it. x and y are two matrices of dimension (n x d) and (n x 1) respectively. Due to this reason, MSE is not suitable for logistic regression. Select the option (s) which is/are correct in such a case. Interview Questions on Logistic Regression - Medium Why cant we use Mean Square Error (MSE) as a cost function for logistic regression. In the cost function for logistic regression, the confident wrong predictions are penalised heavily. 14. Now, you want to add a few new features in the same data. In logistic regression, we use the sigmoid function and perform a non-linear transformation to obtain the probabilities. Now, using the values of the 5 variables given, you get - In the Logistic regression model the value of classier lies between 0 to 1. 300+ TOP Logistic Regression Multiple Choice Questions and Answers Squaring this non-linear transformation will lead to non-convexity with local minimums. The logistic regression assumes that there is minimal or no multicollinearity among the independent variables. 6 Logistic Regression transforms the output probability to be in a range of [0, 1]. The confident right predictions are rewarded less. Cross-entropy or log loss is used as a cost function for logistic regression. . 250+ TOP MCQs on Linear Regression and Answers - FAQs Interview Questions Logistic Regression MCQ - ProgramsBuzz Logistic Regression Cost Function | Machine Learning - YouTube So to establish the hypothesis we also found the Sigmoid function or Logistic function. but instead of giving the exact value as 0 . c) Gradient Descent. B. Cross-entropy or log loss is used as a cost function for logistic regression. Which of the following function is used by logistic regression to convert the probability in the range between [0,1]. In the cost function for logistic regression, the confident wrong predictions are penalised heavily. Why can't we use Mean Square Error (MSE) as a cost function for a) Linear regression. D. All of the above. Regression Multiple Choice Questions and Answers - gkseries Answer: a. Clarification: Linear regression is a simple approach to supervised learning. 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